CSL4050: Data Visualization

Offered in Winter 2023 Semester

Table of Contents


  • Credits L-T-P [C]: 3-0-3 [4.5]
  • Expectation from 4000 level course:
    • 1 Contact Hr + 2 Non-Contact Hr
    • Learn by Assignments/Experiments
  • Where: LHB 307
  • Slot: F (Monday, Tuesday, and Thursday 2:00 PM - 2:50 PM)
    • Lab Slot: Will be Announced
  • LMS: Moodle
    • Credential: Internet ID/Password
    • Easy Enrollment Code: ei5nm4
    • Easy Enrollment QR:


  • Introduction: Data for Graphics, Design principles, Value for visualization, Categorical, time series, and statistical data graphics, Introduction to Visualization Tools
  • Graphics Pipeline: Introduction, Primitives: vertices, edges, triangles, Model transforms: translations, rotations, scaling, View transform, Perspective transform, window transform
  • Aesthetics and Perception: Graphical Perception Theory, Experimentation, and the Application, Graphical Integrity, Layering and Separation, Color and Information, Using Space Effectively
  • Visualization Design: Visual Display of Quantitative Information, Data-Ink Maximization, Graphical Design, Exploratory Data Analysis, Heat Map
  • Multidimensional Data: Query, Analysis and Visualization of Multi-dimensional Relational Databases, Interactive Exploration, tSNE
  • Interaction: Interactive Dynamics for Visual Analysis, Visual Queries, Finding Patterns in Time Series Data, Trend visualization, Animation, Dashboard, Visual Storytelling
  • Collaboration: Graph Visualization and Navigation, Online Social Networks, Social Data Analysis, Collaborative Visual Analytics, Text, Map, Geospatial data

Lab Content

  • Visualization Design, Exploratory data analysis, Interactive Visualization Tools like Tableau, Gephi, D3, etc. Mini Project.
  • We will be using Python Dash, R-Shiny, D3 JS, Grephi in our lab work

Learning Materials


  • E. TUFTE (2001), The Visual Display of Quantitative Information, Graphics Press, 2nd Edition.
  • J. KOPONEN, J. HILDÉN (2019), Data Visualization Handbook, CRC Press.

Reference Books

  • M. LIMA (2014), The Book of Trees: Visualizing Branches of Knowledge, Princeton Architectural Press.
  • R. TAMASSIA (2013), Handbook of Graph Drawing and Visualization, CRC Press.
  • S. MURRAY (2017), Interactive Data Visualization for the Web, O’Reilly Press, 2nd Edition.

Attendance Requirement

As per the notification from academics 100% attendance is mandatory. If you have genuine reason please take leave approval as per academics rule.

If attendance falls below 75%, one should get at least C grade to pass the course. Otherwise F grade will be assigned.

Grading Policy

Quizzes (Moodle) Mini Project Lab Assignments Minors Major
20% 10% 30% 10% + 10% 20%

Quizzes (Moodle)

  • There will be about 3 - 4 quizzes; best 2 will be considered for grading.
  • All the quizzes will be in Moodle Platform.
  • No makeup quiz will be taken considering there will be more than required no of quizzes.

Mini Project

  • Students need to build visualization project as part of the lab exercise
  • Projects can be done in group
  • Max 2 member group is allowed

Lab Assignment

  • Students will be asked to work on different graded assignments
  • Best five scores will be considered for grading
Quiz dates will be announced during class

Plagiarism tolerance is 7% from single source and 15% cumulative, anything more will reduce your marks as follows:

  • Any logical/conceptual/formulation plagiarism: zero marks
  • Other form of plagiarism (above 50%): zero marks
  • Otherwise: Percentage of plagiarism will be deducted from the obtained mark